Text Analytics

Text analytics involves converting unstructured text data into structured, usable information. This process employs software rules and machine learning algorithms to dissect sentences, identify components, and determine their roles and meanings. Essential to natural language processing (NLP), text analytics supports features like named entity recognition, categorization, and sentiment analysis.

• Who is speaking?
• What is the topic?
• What opinions are expressed?
• How is the sentiment?

Professionals, including data analysts, leverage text mining tools for insights from large text datasets like social media, reviews, and news articles. Text analytics underpins business intelligence initiatives, encompassing customer experience management, social listening, media monitoring, and workforce analytics.

Text mining involves collecting valuable information from textual documents in a general sense.

Text analytics encompasses the computational procedures of dissecting unstructured text documents like tweets, articles, reviews, and comments, enabling subsequent in-depth analysis.

The computer’s comprehension of the essence in text documents, involving the identification of speakers, topics, and sentiments, defines text analytics. While the terms text mining and text analytics are often used interchangeably, there is a distinction. Text mining involves gathering useful data from text, while text analytics is the process through which a computer translates raw text into meaningful information. The fundamental computational operations in text analytics underpin various natural language processing features like sentiment analysis, named entity recognition, categorization, and theme analysis.

The computer’s comprehension of the essence in text documents, involving the identification of speakers, topics, and sentiments, defines text analytics. While the terms text mining and text analytics are often used interchangeably, there is a distinction. Text mining involves gathering useful data from text, while text analytics is the process through which a computer translates raw text into meaningful information. The fundamental computational operations in text analytics underpin various natural language processing features like sentiment analysis, named entity recognition, categorization, and theme analysis.

• Language Identification
• Tokenization
• Sentence breaking
• Part of Speech tagging
• Chunking
• Syntax parsing
• Sentence chaining